Shale gas reservoir characterization is important for accurate estimation of the original gas-in-place, the production rates, and the storage capacity of depleted reservoirs. Characterization typically includes laboratory measurements of pore, water, and gas volumes, and sorption capacity of selected shale samples. Conventional methods of sampling and measuring these properties have had limited success due to the tight and multiscale nature of the core samples. Shales commonly have relatively low porosity and ultra-low permeability. In addition, they have pores with a wide range of sizes, which often leads to multimodal pore size distribution. The latter is associated with the diversity of minerals that comprise shale, such as clays, carbonates, and organic material (e.g., kerogen). The complexity in mineral content leads to fundamental questions, and often uncertainties, relating to the calculation of the petrophysical properties, the total amounts and spatial distribution of original fluids in the reservoir, their thermodynamic states (i.e., adsorbed or free), and, finally, the mechanisms of their transport under the reservoir conditions.
A dielectric logging device may be used to measure the formation dielectric constant and conductivity at multiple frequencies from 20 MHz to 1 GHz. One such device features a short, articulated pad, allowing optimal pad contact even in rough boreholes and minimization of environmental effects. Mudcake or other material directly in front of the pad is measured using two transmitters and eight receivers which are mounted on the pad, in addition to a pair of electric dipoles operating in reflection mode. The analysis of the multifrequency dielectric measurements provides information about formation water content, water salinity, and rock texture.
Numerous dielectric forward models have been developed and used to convert the dielectric measurements into water saturation, water salinity, and rock matrix. Certain models are generally known as a bimodal model, a Stroud-Milton-De (SMD) model, a shaly sand model, and a complex refractive index model (CRIM). Each of those models has inherent strengths and weaknesses based on the assumptions intrinsic to the particular model. Some model types (e.g., effective medium and phenomenological) work well with different rock types, taking into account the order and shape of replacement material. Other model types (e.g., empirical and semi-empirical) can accurately predict values for the data used to construct them, but are not widely applicable to data sets consisting of different mineralogies, porosities, or water saturations. The CRIM, for example, falls within this latter category, as it does not account for micro-geometry of the rock components, and does not account for electrochemical interaction between the components.